Curriculum Based Multi-Task Learning for Parkinson's Disease Detection

TL;DR

This study introduces a curriculum learning approach for deep CNNs to improve Parkinson's disease detection from MRI data, leveraging disease severity stages to enhance classifier performance.

Contribution

It proposes a novel curriculum training strategy based on disease severity to improve deep learning classification of Parkinson's disease from MRI images.

Findings

Curriculum learning increased classification ROC AUC by 3.9%.

Deep learning classification remains challenging with MRI alone.

Future multimodal imaging could further improve results.

Abstract

There is great interest in developing radiological classifiers for diagnosis, staging, and predictive modeling in progressive diseases such as Parkinson's disease (PD), a neurodegenerative disease that is difficult to detect in its early stages. Here we leverage severity-based meta-data on the stages of disease to define a curriculum for training a deep convolutional neural network (CNN). Typically, deep learning networks are trained by randomly selecting samples in each mini-batch. By contrast, curriculum learning is a training strategy that aims to boost classifier performance by starting with examples that are easier to classify. Here we define a curriculum to progressively increase the difficulty of the training data corresponding to the Hoehn and Yahr (H&Y) staging system for PD (total N=1,012; 653 PD patients, 359 controls; age range: 20.0-84.9 years). Even with our multi-task setting using pre-trained CNNs and transfer learning, PD classification based on T1-weighted (T1-w) MRI was challenging (ROC AUC: 0.59-0.65), but curriculum training boosted performance (by 3.9%) compared to our baseline model. Future work with multimodal imaging may further boost performance.